ARTICLE IN PRESS H.-F. Wang. C.-T. Wu Computers Ope The usual techniques used to maintain user's profiles are prevent poor prediction due to rarely relevant information [44]. history-based model [37] and the vector space m because the conventional CF approach utilizes A history-based model lists purchase rec bors to make a prediction for a target user, of non-neighbors out of considera- the impacts of ng the effects could be also space mo for large am adopted in t 2.2. Output of In g information simplest item. A s consider it or unord advertising st tion, displ cross-selling recommenda are not rel promotions. I products that 2.3. Recon Recommer and efficiency items accc know us use a rela mapping purchased lles fore, of ed by es involved: the le 1, we list of EC constructed ded as built Shih developed Please cite this article as -commerce. Computers and OperaARTICLE IN PRESS The usual techniques used to maintain user’s profiles are the history-based model [37] and the vector space model [11,40]. A history-based model lists purchase records, navigation history, or the contents of e-mail boxes to define users’ profiles. In the vector space model, items are represented with a vector of features or attributes, usually words or concepts (such as a binary column to denote the purchased state or a column to denote the attributive value of an item), with an associated value. The vector space model is more efficient for computation, so it is often used for large amounts of data. For this reason, it is also the model adopted in this paper to maintain the database. 2.2. Output of recommendations In general, the output is a suggestion of product(s) containing information on item type, quantity, and appearance [46]. The simplest form of a suggestion is the recommendation of a single item. A single item increases the chance that a user will seriously consider it desirable. More commonly, an RS provide an ordered or unordered recommendation list for a user [38]. Some advertising strategies can also be embedded in the recommenda￾tion, displaying bundled items, which could help enhance cross-selling and up-selling. By comparing bundled items with a recommendation list, bundled items may include products that are not related to the users since they are generated for promotions. In contrast, a recommendation list shows a set of products that satisfies users’ preferences to a certain degree. 2.3. Recommendation methods Recommendation methods are concerned with the accuracy and efficiency of prediction and presentation of the recommended items according to users’ input sources. For an RS, it is critical to know users’ preferences systematically. An essential concept is to use a relational database which is constructed offline. Then by mapping a new user to the database, a product that has been purchased by the same type of historical users can easily be picked up for the target user [29]. Clustering analysis is the technique that groups users/items with similar characteristics/properties into one group. By clustering, the search dimensionality can be reduced which speeds up the mapping process. A wide range of applications have been implemented by clustering techniques, and one of these is used to predict unknown users based on the group they belong to [49]. By analyzing the properties of the groups, we can learn about the characteristics of new users by identifying the group they belong to and thus provide them with the items that the same group has mostly bought. Besides, clustering analysis is also a very useful tool for looking for the ‘‘neighbors’’ in the information filtering technology. That is, the users called the neighbors are chosen by certain methods, such as clustering techniques, to support the prediction [6]. Information filtering technology has the ability to define user preferences with little effort. It is divided into two main categories [26]—collaborative filtering (CF) and content-based filtering (CBF). CF is the most popular approach to predict the probability that a user will purchase a specific item based on other users’ preferences [21]. A CF method functions by matching people with similar interests and then making recommendations. However, in the initial state of an RS, the main problem would be insufficient users’ profiles sustain the prediction basis while using CF. Consequently, the drawback of CF is the requirement of some relevant rating data given by the target user. Usually, by clustering users into groups before predicting, group influences could be utilized by recommendation methods on the target user to prevent poor prediction due to rarely relevant information [44]. Furthermore, because the conventional CF approach utilizes preferences of neighbors to make a prediction for a target user, it leaves additional influences of non-neighbors out of considera￾tion. As a result, research tends to discriminate the impacts of neighbors from non-neighbors [23]; by integrating the effects caused by the two sources, better performance could be also expected. CBF is the technology of analysis based on terms in the content such as texts or documents on the Web site. It considers term frequency in the content and its relation to the user’s preference. However, with other media such as music or movies, its performance is not as good as text content because these objects are not easily indexed. In addition, the maintenance of numerous heterogeneous electronic product catalogues on the Internet is still a tough task [16]. Nevertheless, CF is still most commonly used since it is flexible and easily adaptable to an EC’s RS [7]. Therefore, in this paper, we would incorporate the concept of CF into our system as the basic recommendation mechanism. In addition to CF and CBF, another technique requires the private information of a user. Demographic filtering (DF) explains users by their personal demographics [17]. A DF approach uses descriptions of people to learn the probability that an item is most preferred by what type of persons. Therefore, this method would lead to the same recommendation if the users have similar personal data. However, the DF approach requires more informa￾tion regarding a user’s privacy; therefore, DF is confronted with the problem that it is not easy to collect users’ demographic descriptions. Consequently, the DF method requires collaborating with other methods such as CF or CBF [37]. Besides the aforementioned filtering techniques, rules derived from the market basket analysis between items in large databases also account for an RS. Market basket analysis has been a popular system in finding the correlation among baskets [2,41]. One of the techniques is the famous association rules method, which was first introduced by Agrawal et al. [3]. Association rules have been used to find the pattern of the probability of buying a specific product when another product is purchased. In such a recommending environment, many rules have been developed on how the different purchase behaviors of users can be treated [20]. Therefore, Sarwar et al. also proposed a method of association￾rule based recommendation (ABR) in 2000 [42]. However, for the huge amount of transaction data, there may be many biased rules that would affect the precision of the recommendation. Therefore, the market basket analysis shall be conducted with the aid of filtering techniques such as CF, and the common concept of the CF method adapted to the binary market basket data as proposed by Mild and Reutterer [36]. 2.4. Roles with their goals in a recommender system In the current RS, there are three common roles involved: the supplier, the system developer, and the user. In Table 1, we list possible considerations for constructing an RS. In the fields of EC trading, Li and Wang proposed a multi-agent-based model with a win-win negotiation approach of which the agents seek to strike a fair deal that also maximizes the payoff for everyone involved [30]. However, such kind of win-win negotiation mechanism has not been discussed in the RSs with more comprehensive scale. For the existing research, the ‘‘performance of recommendation’’ is an attribute that benefits users. Therefore, when ‘‘more is better’’ is stressed, only the number of sold products is maximized but not necessarily the profit. In other words, an RS is usually constructed from a user’s standpoint. Only a few RSs could be regarded as built from a supplier’s perspective. For instance, Liu and Shih developed H.-F. Wang, C.-T. Wu / Computers & Operations Research ] (]]]]) ]]]–]]] 3 Please cite this article as: Wang H-F, Wu C-T. A strategy-oriented operation module for recommender systems in E-commerce. Computers and Operations Research (2010), doi:10.1016/j.cor.2010.03.011